In short
Agentic AI is artificial intelligence that acts autonomously toward a goal, planning its own steps and using tools to get there, rather than only producing a single response on request. Where generative AI gives you an output (a paragraph, an image, a block of code) for each prompt, agentic AI takes a goal and drives a multi-step process to achieve it, deciding what to do, doing it, checking the result and continuing. In short, generative AI creates content; agentic AI gets things done. It is the broader capability that individual AI agents put into practice.
Agentic AI vs generative AI
They are not opposites; agentic AI is usually built on top of generative models. The distinction is what the system does with the model. Generative AI responds. Agentic AI acts: it sets sub-goals, calls tools, and adapts based on what it observes, all to reach an outcome you defined once.
- Generative AI: prompt in, content out. One step, no actions on the world.
- Agentic AI: goal in, outcome out. Many steps, real actions, self-correction.
- An "AI agent" is a single concrete system that exhibits agentic behaviour.
What makes a system agentic
Three properties show up across agentic systems: autonomy (it chooses its own next step), tool use (it can act, not just talk), and a feedback loop (it observes results and adjusts). The more of a task a system can carry without you steering each step, the more agentic it is. People often describe this on a spectrum from a simple assistant up to fully autonomous multi-agent systems.
Where you already see it
Coding agents that take a ticket and open a working pull request, research agents that gather and synthesise sources, and automation flows that watch for an event and complete a multi-step task are all agentic AI. For builders, the practical takeaway is that agentic AI shifts your job from writing every instruction to setting clear goals, providing the right tools and context, and keeping verification in the loop.